Sound event detection in real life audio

In this paper, we propose the use of spatial and harmonic features in
combination with long short term memory (LSTM) recurrent neural
network (RNN) for automatic sound event detection (SED) task.
Real life sound recordings typically have many overlapping sound
events, making it hard to recognize with just mono channel audio.
Human listeners have been successfully recognizing the mixture of
overlapping sound events using pitch cues and exploiting the stereo
(multichannel) audio signal available at their ears to spatially localize
these events. Traditionally SED systems have only been using
mono channel audio, motivated by the human listener we propose to
extend them to use multichannel audio. The proposed SED system
is compared against the state of the art mono channel method on the
development subset of TUT sound events detection 2016 database
[1]. The usage of spatial and harmonic features are shown to improve
the performance of SED.